5-13Mart Anthony D. dela Pena2/17/15ForecastingMoving averages - 4 period moving average

Num pds4Data
Julius Rey: Forecasting: Submodel = 11; Problem size @ 12 by 4Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrPeriod 110Period 212Period 313Period 416Period 51912.756.256.2539.062532.89%Period 62315886434.78%Period 72617.758.258.2568.062531.73%Period 83021998130.00%Period 92824.53.53.512.2512.50%Period 101826.75-8.758.7576.562548.61%Period 111625.5-9.59.590.2559.38%Period 121423-998164.29%Total7.7562.25512.1875314.18%Average0.968757.7812564.023437539.27%BiasMADMSEMAPESE9.2392956081Next period19

Enter the past demands in the data area

5-14Mart Anthony D. dela Pena2/17/15ForecastingMoving averages - 3 period moving average

Num pds3Data
Julius Rey: Forecasting: Submodel = 11; Problem size @ 12 by 3Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrPeriod 110Period 212Period 313Period 41611.66666666674.33333333334.333333333318.777777777827.08%Period 51913.66666666675.33333333335.333333333328.444444444428.07%Period 62316774930.43%Period 72619.33333333336.66666666676.666666666744.444444444425.64%Period 83022.66666666677.33333333337.333333333353.777777777824.44%Period 92826.33333333331.66666666671.66666666672.777777777805.95%Period 101828-101010055.56%Period 111625.3333333333-9.33333333339.333333333387.111111111158.33%Period 121420.6666666667-6.66666666676.666666666744.444444444447.62%Total6.333333333358.3333333333428.7777777778303.13%Average0.70370370376.481481481547.641975308633.68%BiasMADMSEMAPESE7.8264914396Next period16

Enter the past demands in the data area

5-15aMart Anthony D. dela Pena2/17/15ForecastingMoving averages - 3 period moving average

Num pds3Data
Julius Rey: Forecasting: Submodel = 11; Problem size @ 11 by 3Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrPeriod 14Period 26Period 34Period 454.66666666670.33333333330.33333333330.111111111106.67%Period 5105552550.00%Period 686.33333333331.66666666671.66666666672.777777777820.83%Period 777.6666666667-0.66666666670.66666666670.444444444409.52%Period 898.33333333330.66666666670.66666666670.444444444407.41%Period 9128441633.33%Period 10149.33333333334.66666666674.666666666721.777777777833.33%Period 111511.66666666673.33333333333.333333333311.111111111122.22%Total1920.333333333377.6666666667183.32%Average2.3752.54166666679.708333333322.92%BiasMADMSEMAPESE3.5978388575Next period13.6666666667

Enter the past demands in the data area

5-15bMart Anthony D. dela Pena2/17/15ForecastingWeighted moving averages - 3 period moving average

Data
Julius Rey: Forecasting: Submodel = 12; Problem size @ 11 by 3Forecasts and Error AnalysisPeriodDemandWeightsForecastErrorAbsoluteSquaredAbs Pct ErrPeriod 141Period 261Period 342Period 454.50.50.50.2510.00%Period 5105552550.00%Period 687.250.750.750.562509.38%Period 777.75-0.750.750.562510.71%Period 89811111.11%Period 9128.253.753.7514.062531.25%Period 101410441628.57%Period 111512.252.752.757.562518.33%Total1718.565169.36%Average2.1252.31258.12521.17%BiasMADMSEMAPESE3.291402943Next period14

Enter the data in the shaded area.. Enter weights in INCREASING order from top to bottom.

5-18Mart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.3Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 11 by 1Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrPeriod 145-11125.00%Period 264.71.31.31.6921.67%Period 345.09-1.091.091.188127.25%Period 454.7630.2370.2370.05616904.74%Period 5104.83415.16595.165926.6865228151.66%Period 686.383871.616131.616132.611876176920.20%Period 776.8687090.1312910.1312910.017237326701.88%Period 896.90809632.09190372.09190374.376061090123.24%Period 9127.535667414.464332594.4643325919.930265474137.20%Period 10148.8749671875.1250328135.12503281326.265961334336.61%Period 111510.41247703094.58752296914.587522969121.0453669920.3058348646Total22.629113072126.8091130721104.8675602041280.03%Average2.05719209752.43719209759.53341456425.46%BiasMADMSEMAPESE3.4134954422Next period11.7887339216

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

5-19Mart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.3Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 5 by 1Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrYear 14504104040160008.89%Year 24954227373532914.75%Year 3518443.974.174.15490.8114.31%Year 4563466.1396.8796.879383.796917.21%Year 5584495.19188.80988.8097887.0384810.1520702055Total372.779372.77929690.64538170.35%Average74.555874.55585938.129076214.07%BiasMADMSEMAPESE99.4830729002Next period521.8337

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

5-20aMart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.6Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 5 by 1Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrYear 14504104040160008.89%Year 24954346161372112.32%Year 3518470.647.447.42246.7609.15%Year 4563499.0463.9663.964090.881611.36%Year 5584537.41646.58446.5842170.0690560.0797671233Total258.944258.94413828.71065649.70%Average51.788851.78882765.742131209.94%BiasMADMSEMAPESE67.8938157616Next period565.3664

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

5-20bMart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.9Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 5 by 1Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrYear 14504104040160008.89%Year 24954464949240109.90%Year 3518490.127.927.9778.4105.39%Year 4563515.2147.7947.792283.884108.49%Year 5584558.22125.77925.779664.5568410.0441421233Total190.469190.4697727.85094137.08%Average38.093838.09381545.570188207.42%BiasMADMSEMAPESE50.7538206805Next period581.4221

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

5-22Mart Anthony D. dela PenaSimple Linear Regression2/17/15ForecastingMoving averages - 3 period moving average

Num pds3Data
Julius Rey: Forecasting: Submodel = 11; Problem size @ 5 by 3Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrYear 1450Year 2495Year 3518Year 4563487.666666666775.333333333375.33333333335675.111111111113.38%Year 5584525.333333333358.666666666758.66666666673441.777777777810.05%Total1341349116.888888888923.43%Average67674558.444444444411.71%BiasMADMSEMAPESEERROR:#DIV/0!Next period555Not enough data to compute the standard error

Enter the past demands in the data area

5-23Mart Anthony D. dela Pena2/17/15ForecastingSimple linear regression

Data
Julius Rey: Forecasting: Submodel = 15; Problem size @ 5 by 3Forecasts and Error AnalysisPeriodDemand (y)Period(x)ForecastErrorAbsoluteSquaredAbs Pct ErrYear 14501454.8-4.84.823.0401.07%Year 24952488.46.66.643.5601.33%Year 35183522-441600.77%Year 45634555.67.47.454.7601.31%Year 55845589.2-5.25.227.0400.89%Total-028164.405.38%Intercept421.2Average-05.632.8801.08%Slope33.6BiasMADMSEMAPESE7.4027022093Forecast622.86Correlation0.99279753Coefficient of determination0.9856469356

If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y.

5-25aMart Anthony D. dela Pena2/17/15ForecastingMoving averages - 3 period moving average

Num pds3Data
Julius Rey: Forecasting: Submodel = 11; Problem size @ 13 by 3Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrPeriod 111Period 214Period 316Period 41013.6666666667-3.66666666673.666666666713.444444444436.67%Period 51513.33333333331.66666666671.66666666672.777777777811.11%Period 61713.66666666673.33333333333.333333333311.111111111119.61%Period 71114-33927.27%Period 81414.3333333333-0.33333333330.33333333330.111111111102.38%Period 9171433917.65%Period 101214-22416.67%Period 111414.3333333333-0.33333333330.33333333330.111111111102.38%Period 121614.33333333331.66666666671.66666666672.777777777810.42%Period 131114-33927.27%Total-2.66666666672261.3333333333171.42%Average-0.26666666672.26.133333333317.14%BiasMADMSEMAPESE2.768874621Next period13.6666666667

Enter the past demands in the data area

5-25bMart Anthony D. dela Pena2/17/15ForecastingWeighted moving averages - 3 period moving average

Data
Julius Rey: Forecasting: Submodel = 12; Problem size @ 13 by 3Forecasts and Error AnalysisPeriodDemandWeightsForecastErrorAbsoluteSquaredAbs Pct ErrPeriod 1111Period 2142Period 3163Period 41014.5-4.54.520.2545.00%Period 51512.66666666672.33333333332.33333333335.444444444415.56%Period 61713.53.53.512.2520.59%Period 71115.1666666667-4.16666666674.166666666717.361111111137.88%Period 81413.66666666670.33333333330.33333333330.111111111102.38%Period 91713.53.53.512.2520.59%Period 101215-33925.00%Period 11141400000.00%Period 121613.83333333332.16666666672.16666666674.694444444413.54%Period 131114.6666666667-3.66666666673.666666666713.444444444433.33%Total-3.527.166666666794.8055555556213.87%Average-0.352.71666666679.480555555621.39%BiasMADMSEMAPESE3.442483761Next period13.1666666667

Enter the data in the shaded area.. Enter weights in INCREASING order from top to bottom.

5-26Mart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.2Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 12 by 1Forecasts and Error AnalysisTracking SignalPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrCum ErrorCum Abs ErrMadTrack Signal (Cum Error/MAD)Week 1171700000.00%Week 22117441619.05%4422Week 31917.81.21.21.4406.32%5.25.21.73333333333Week 42318.044.964.9624.601621.57%10.1610.162.544Week 51819.032-1.0321.0321.06502405.73%9.12811.1922.23844.0779127949Week 61618.8256-2.82562.82567.9840153617.66%6.302414.01762.33626666672.697637256Week 72018.260481.739521.739523.025929830408.70%8.0419215.757122.25101714293.5725716375Week 81818.608384-0.6083840.6083840.370131091503.38%7.43353616.3655042.0456883.6337584226Week 92218.48670723.51329283.513292812.343226298515.97%10.946828819.87879682.20875524.956107766Week 102019.189365760.810634240.810634240.657127871104.05%11.7574630420.689431042.0689431045.6828353652Week 111519.351492608-4.3514926084.35149260818.935487917529.01%7.40597043225.0409236482.27644760443.2533015114Week 122218.48119408643.51880591363.518805913612.38199505760.159945723310.924776345628.55972956162.37997746354.590285628Total10.924776345628.559729561698.8045374265147.43%Average0.91039802882.37997746358.233711452212.29%BiasMADMSEMAPESE3.1433189057Next period19.1849552691

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

5-27aMart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.1Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 24 by 1Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrWeek 1505000000.00%Week 23550-151522542.86%Week 32548.5-23.523.5552.2594.00%Week 44046.15-6.156.1537.822515.38%Week 54545.535-0.5350.5350.28622501.19%Week 63545.4815-10.481510.4815109.8618422529.95%Week 72044.43335-24.4333524.43335596.9885922225122.17%Week 83041.990015-11.99001511.990015143.760459700239.97%Week 93540.7910135-5.79101355.791013533.535837357216.55%Week 102040.21191215-20.2119121520.21191215408.5213927593101.06%Week 111538.190720935-23.19072093523.190720935537.809537485154.60%Week 124035.87164884154.12835115854.128351158517.043283287910.32%Week 135536.284483957418.715516042618.7155160426350.270540742734.03%Week 143538.1560355616-3.15603556163.15603556169.960560466209.02%Week 152537.8404320055-12.840432005512.8404320055164.876694086751.36%Week 165536.556388804918.443611195118.4436111951340.166793915733.53%Week 175538.400749924416.599250075616.5992500756275.535103071730.18%Week 184040.060674932-0.0606749320.0606749320.003681447400.15%Week 193540.0546074388-5.05460743885.054607438825.549056360214.44%Week 206039.549146694920.450853305120.4508533051418.237400906734.08%Week 217541.594232025433.405767974633.40576797461115.945333972144.54%Week 225044.93480882295.06519117715.065191177125.656161660910.13%Week 234045.4413279406-5.44132794065.441327940629.60804975713.60%Week 246544.897195146520.102804853520.1028048535404.12276297690.3092739208Total-30.9252436813304.74793524555822.8118094263934.03%Average-1.288551820112.6978306352242.617158726138.92%BiasMADMSEMAPESE16.2687818863Next period46.9074756319

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

5-31Mart Anthony D. dela Pena2/17/15ForecastingDecomposition, multiplicative

4 seasons

Data
Julius Rey: Forecasting: Submodel = 18; Problem size @ 16 by 4Forecasts and Error AnalysisPeriodDemand (y)Time (x)AverageRatioSeasonalSmoothedUnadjustedAdjustedError|Error|Error^2Abs Pct ErrPeriod 121810.8824512456247.0391436183241.4136392516213.03576666444.96423333564.964233335624.6436126102.28%Period 224720.9816478215251.6177335497245.079424247240.58168291646.41831708366.418317083641.19479418502.60%Period 32433250250.8750.96860986550.9711636765250.2152890213248.7452092425241.57231191731.42768808271.42768808272.038293261600.59%Period 42924251.75252.6251.15586343391.1569079047252.3969270251252.4109942379292.0162744698-0.01627446980.01627446980.000264858400.01%Period 52255253.52550.88235294120.8824512456254.971593184256.0767792334225.9752728059-0.97527280590.97527280590.95115704600.43%Period 62546256.5257.3750.98688683830.9816478215258.7486004924259.7425642288254.9757223365-0.97572233650.97572233650.95203407800.38%Period 72557258.25259.3750.98313253010.9711636765262.5715995902263.4083492243255.8126208509-0.81262085090.81262085090.660352647300.32%Period 82998260.5261.8751.14176610981.1569079047258.4475382894267.0741342197308.9801770226-9.98017702269.980177022699.603933402303.34%Period 92349263.25264.3750.8851063830.8824512456265.1704569113270.7399192152238.9147789474-4.91477894744.914778947424.155052101802.10%Period 1026510265.52690.98513011150.9816478215269.9542485452274.4057042106269.3697617566-4.36976175664.369761756619.094817809601.65%Period 1126411272.5274.50.96174863390.9711636765271.8388325169278.0714892061270.0529297845-6.05292978456.052929784536.637958976502.29%Period 1232712276.5278.751.17309417041.1569079047282.6499833466281.7372742015325.94407957541.05592042461.05592042461.11496794300.32%Period 1325013281284.1250.87989441270.8824512456283.3017702044285.403059197251.8542850889-1.85428508891.85428508893.438373190800.74%Period 1428314287.25290.8750.97292651480.9816478215288.2907635408289.0688441924283.7638011767-0.76380117670.76380117670.583392237500.27%Period 1528915294.50.9711636765297.5811462023292.7346291879284.29323871824.70676128184.706761281822.153601764201.63%Period 16356161.1569079047307.7168014415296.4004141834342.907982128313.092017871713.0920178717171.400931953603.68%Total0.949313840162.3805623198448.623538065622.63%AverageIntercept237.74785425610.0593321153.89878514528.038971129101.41%Slope3.6657849955BiasMADMSEMAPESE6.6979365335RatiosSeason 1Season 2Season 3Season 40.96860986551.15586343390.88235294120.98688683830.98313253011.14176610980.8851063830.98513011150.96174863391.17309417040.87989441270.9729265148Average0.88245124560.98164782150.97116367651.15690790473.9921706484ForecastsPeriodUnadjustedSeasonalAdjusted17300.06619917880.8824512456264.793791230418303.73198417430.9816478215298.157840596819307.39776916970.9711636765298.533547651820311.06355416521.1569079047359.8718846811

Enter past demands in the data area. Do not change the time period numbers!

5-27bMart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.6Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 24 by 1Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrWeek 1505000000.00%Week 23550-151522542.86%Week 32541-161625664.00%Week 44031.48.68.673.9621.50%Week 54536.568.448.4471.233618.76%Week 63541.624-6.6246.62443.87737618.93%Week 72037.6496-17.649617.6496311.5083801688.25%Week 83027.059842.940162.940168.644540825609.80%Week 93528.8239366.1760646.17606438.143766532117.65%Week 102032.5295744-12.529574412.5295744156.990234645162.65%Week 111525.01182976-10.0118297610.01182976100.236735143266.75%Week 124019.00473190420.99526809620.995268096440.801282422952.49%Week 135531.601892761623.398107238423.3981072384547.471422339742.54%Week 143545.6407571046-10.640757104610.6407571046113.225711759930.40%Week 152539.2563028419-14.256302841914.2563028419203.242170718757.03%Week 165530.702521136724.297478863324.2974788633590.367479110444.18%Week 175545.28100845479.71899154539.718991545394.458796657717.67%Week 184051.1124033819-11.112403381911.1124033819123.485508921627.78%Week 193544.4449613528-9.44496135289.444961352889.20729495526.99%Week 206038.777984541121.222015458921.2220154589450.373940137835.37%Week 217551.511193816423.488806183623.4888061836551.724015928831.32%Week 225065.6044775266-15.604477526615.6044775266243.499718877431.21%Week 234056.2417910106-16.241791010616.2417910106263.79577523340.60%Week 246546.496716404318.503283595718.5032835957342.37150382470.2846659015Total12.6644776028322.89587235955339.6192541937877.17%Average0.527686566813.4539946816222.484135591436.55%BiasMADMSEMAPESE15.5791516489Next period57.5986865617

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

5-28Mart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.9Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 24 by 1Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrWeek 1505000000.00%Week 23550-151522542.86%Week 32536.5-11.511.5132.2546.00%Week 44026.1513.8513.85191.822534.63%Week 54538.6156.3856.38540.76822514.19%Week 63544.3615-9.36159.361587.6376822526.75%Week 72035.93615-15.9361515.93615253.960876822579.68%Week 83021.5936158.4063858.40638570.667308768228.02%Week 93529.15936155.84063855.840638534.113058087716.69%Week 102034.41593615-14.4159361514.41593615207.819215080972.08%Week 111521.441593615-6.4415936156.44159361541.494128300842.94%Week 124015.644159361524.355840638524.3558406385593.20697320860.89%Week 135537.564415936217.435584063817.4355840638303.999591647631.70%Week 143553.2564415936-18.256441593618.2564415936333.297659661152.16%Week 152536.8256441594-11.825644159411.8256441594139.845859783847.30%Week 165526.182564415928.817435584128.8174355841830.444593641752.40%Week 175552.11825644162.88174355842.88174355848.304445936405.24%Week 184054.7118256442-14.711825644214.7118256442216.437813784136.78%Week 193541.4711825644-6.47118256446.471182564441.87620378218.49%Week 206035.647118256424.352881743624.3528817436593.062849215740.59%Week 217557.564711825617.435288174417.4352881744303.989273722823.25%Week 225073.2564711826-23.256471182623.2564711826540.863451865446.51%Week 234052.3256471183-12.325647118312.3256471183151.921576883830.81%Week 246541.232564711823.767435288223.7674352882564.89098017760.3656528506Total14.0258405235333.03062457835907.6742676202886.52%Average0.584410021813.8762760241246.153094484236.94%BiasMADMSEMAPESE16.3869047879Next period62.6232564712

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

5-29Mart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.1Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 6 by 1Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrMonth 17065552507.14%Month 268.565.533904.38%Month 364.865.8-11101.54%Month 471.765.7663608.37%Month 571.366.3552507.01%Month 672.866.866360.0824175824Total242613236.69%Average44.33333333332206.11%BiasMADMSEMAPESE5.7445626465Next period67.4

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

5-30Mart Anthony D. dela Pena2/17/15ForecastingExponential smoothing

Alpha0.3Data
Julius Rey: Forecasting: Submodel = 13; Problem size @ 6 by 1Forecasts and Error AnalysisPeriodDemandForecastErrorAbsoluteSquaredAbs Pct ErrMonth 17065552507.14%Month 268.566.522402.92%Month 364.867.1-2.32.35.2903.55%Month 471.766.415.295.2927.984107.38%Month 571.367.9973.3033.30310.90980904.63%Month 672.868.98793.81213.812114.532106410.052364011Total17.105121.705187.7160154130.86%Average2.850853.617516666714.619335901705.14%BiasMADMSEMAPESE4.6828414294Next period70.13153

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.